Soft Tissue Surgical Margin Status is a Powerful Predictor of Outcomes After Radical Cystectomy: A Multicenter Study of More Than 4,400 Patients
Bibliographic record
Abstract
PURPOSE: We evaluated the association of soft tissue surgical margins with characteristics and outcomes of patients treated with radical cystectomy for urothelial carcinoma of the bladder. MATERIALS AND METHODS: We retrospectively collected the data of 4,410 patients treated with radical cystectomy and pelvic lymphadenectomy without neoadjuvant chemotherapy at 12 academic centers in the United States, Canada and Europe. A positive soft tissue surgical margin was defined as presence of tumor at inked areas of soft tissue on the radical cystectomy specimen. RESULTS: Positive soft tissue surgical margins were identified in 278 patients (6.3%). On univariate analysis positive soft tissue surgical margin was significantly associated with advanced pT stage, higher tumor grade, lymphovascular invasion and lymph node metastasis (p <0.001). Actuarial 5-year recurrence-free and cancer specific survival probabilities were 62.8% +/- 0.8% and 69% +/- 0.8% for patients without soft tissue surgical margins vs 21.6% +/- 3.1% and 26.4% +/- 3.3% for those with positive soft tissue surgical margins (p <0.001). On multivariable analyses adjusting for the effect of standard clinicopathological features and adjuvant chemotherapy positive soft tissue surgical margin was an independent predictor of disease recurrence and cancer specific mortality (HR 1.52 and HR 1.51, p <0.001, respectively). Soft tissue surgical margin retained independent predictive value in subgroups with advanced disease such as pT3Nany, pT4Nany or Npositive. CONCLUSIONS: Positive soft tissue surgical margin is a strong predictor of recurrence and eventual death from urothelial carcinoma of the bladder. Soft tissue surgical margin status should always be reported in the pathological reports after radical cystectomy. Due to uniformly poor outcomes patients with positive soft tissue surgical margins should be considered for studies on adjuvant local and/or systemic therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".